1. A system for assessing a person's stress level in real time comprising: a wearable device comprises one or more sensors interconnected to a person's body area, or arm area, or face and body area; one or more sensors interconnected to a person's body area, or skin area, or head and body area, or both; a wireless transceiver interconnected to the one or more sensors; a processor included in the system capable of: receiving a stream of medical data; a control unit comprises an artificial intelligence and machine learning infrastructure that trains a machine-learned model to extract a number of physiological characteristics from the stream of medical data, wherein the machine learning infrastructure trains the machine-learned model based on training data sets that define features stress level; analysing the number of physiological characteristics using a statistical analysis; and executes a simulation process to calculate a person's current degree of stress based on a result of the statistical analysis.

2. The system of claim 1, wherein the physiological data is captured with the aid of an array of sensors.

3. The system of claim 1, wherein the one or more sensors comprises a wrist sensor and the pulse palpation rate comprises a band of the wristwatch, the wristband, or fabric, which a wristband, or fabric is fastened to.

4. The system of claim 2, wherein the plurality of physiological indicators are selected from the group consisting of heart rate, respiration rate, blood pressure, blood oxygen saturation, blood oxygen saturation, and blood oxygen saturation.

5. The system as recited in claim 1, further comprising a smart phone connected to the processor adapted to receive the physiological data from the sensor and transmit the physiological data to the smart phone.

6. A method for assessing a person's stress level in real time comprising: receiving a stream of medical data from one or more sensors of a wearable device; extracting a number of physiological characteristics from the stream of medical data using an artificial intelligence and machine learning infrastructure; analysing the number of physiological characteristics using a statistical analysis; and executes a simulation process to calculate a person's current degree of stress based on a result of the statistical analysis.

7. The method of claim 6, wherein the physiological data is captured with the aid of an array of sensors.

8. The method of claim 6, wherein the one or more sensors comprises a wrist sensor and the pulse palpation rate comprises a band of the wristwatch, the wristband, or fabric, which a wristband, or fabric is fastened to.

9. The method of claim 6, wherein the plurality of physiological indicators are selected from the group consisting of heart rate, respiration rate, blood pressure, blood oxygen saturation, blood oxygen saturation, and blood oxygen saturation.   WEARABLE DEVICE TO DETERMINE STRESS LEVEL Abstract A wearable device may comprise one or more sensors coupled to a person's body region, or arm area, or face and body area, to provide a real-time assessment of that person's stress level. In certain implementations, the one or more sensors are linked together through a wireless transceiver. In certain implementations, a medical data stream may be received by a processor built into the system. Physiological features may be extracted from a stream of medical data using a machine-learned model trained with the help of artificial intelligence and machine learning. , Claims:Claims :

1. A system for assessing a person's stress level in real time comprising: a wearable device comprises one or more sensors interconnected to a person's body area, or arm area, or face and body area; one or more sensors interconnected to a person's body area, or skin area, or head and body area, or both; a wireless transceiver interconnected to the one or more sensors; a processor included in the system capable of: receiving a stream of medical data; a control unit comprises an artificial intelligence and machine learning infrastructure that trains a machine-learned model to extract a number of physiological characteristics from the stream of medical data, wherein the machine learning infrastructure trains the machine-learned model based on training data sets that define features stress level; analysing the number of physiological characteristics using a statistical analysis; and executes a simulation process to calculate a person's current degree of stress based on a result of the statistical analysis.

2. The system of claim 1, wherein the physiological data is captured with the aid of an array of sensors.

3. The system of claim 1, wherein the one or more sensors comprises a wrist sensor and the pulse palpation rate comprises a band of the wristwatch, the wristband, or fabric, which a wristband, or fabric is fastened to.

4. The system of claim 2, wherein the plurality of physiological indicators are selected from the group consisting of heart rate, respiration rate, blood pressure, blood oxygen saturation, blood oxygen saturation, and blood oxygen saturation.

5. The system as recited in claim 1, further comprising a smart phone connected to the processor adapted to receive the physiological data from the sensor and transmit the physiological data to the smart phone.

6. A method for assessing a person's stress level in real time comprising: receiving a stream of medical data from one or more sensors of a wearable device; extracting a number of physiological characteristics from the stream of medical data using an artificial intelligence and machine learning infrastructure; analysing the number of physiological characteristics using a statistical analysis; and executes a simulation process to calculate a person's current degree of stress based on a result of the statistical analysis.

7. The method of claim 6, wherein the physiological data is captured with the aid of an array of sensors.

8. The method of claim 6, wherein the one or more sensors comprises a wrist sensor and the pulse palpation rate comprises a band of the wristwatch, the wristband, or fabric, which a wristband, or fabric is fastened to.

9. The method of claim 6, wherein the plurality of physiological indicators are selected from the group consisting of heart rate, respiration rate, blood pressure, blood oxygen saturation, blood oxygen saturation, and blood oxygen saturation.

Description:WEARABLE DEVICE TO DETERMINE STRESS LEVEL
Field of the Invention
[0001] The present invention relates generally to monitoring of stress level of user.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Stress and psychology are highly correlated. Stress can have a significant impact on a person's mental and emotional state, and can lead to the development of psychological problems such as anxiety and depression. Prolonged exposure to stress can also lead to physical health problems such as high blood pressure, heart disease, and weakened immune system.
[0004] Understanding an individual's general health status may require monitoring their physiological state. For instance, it might be necessary to track a person's physiological signs, like their electrocardiogram (ECG) data, to determine their level of stress. Portable measurement devices, which can be attached to a user's clothing or body, are one method of monitoring and measuring physiological signals.
[0005] Psychological factors can also impact a person's experience of stress. For example, individuals with certain personality traits or mental health conditions may be more susceptible to experiencing stress and may have a harder time coping with stress than others. Therefore, it is important to address both the psychological and physical aspects of stress in order to promote overall well-being and reduce the negative impact of stress on a person's mental and physical health.
[0006] Physiological signals can be measured over time directly from the body of the individual, for example, by electrodes or sensors in direct contact with the skin of the individual, to obtain an ECG plot. Participants can also be made to experience stress by participating in stress tests or relaxation exercises. The individual's health is then determined by further analysis of the plot by a medical expert.
[0007] These existing methods, however, are complex and time consuming.
[0008] Various technological solutions (e.g., Continuous monitoring of stress using self-reported psychological or behavioural data, stress monitoring system, etc.) are disclosed in patent literature. Few of the exemplary documents are discussed below.
[0009] EP2698112B1 ( By: TATA CONSULTANCY SERVICES) relates to a computer implemented method for real time determination of stress levels of an individual. The method includes receiving at least one stream of physiological data from at least one primary sensor for a predetermined duration, and preprocessing the at least one stream of physiological data to extract physiological parameters, where the preprocessing includes performing a preliminary analysis on the at least one stream of physiological data. The method further includes determining a stress level of the individual based on at least the physiological parameters, wherein the determining comprises performing a statistical analysis on the physiological parameters.
[00010] US8622899B2 ( By: FUJITSU) relates to a method includes accessing data streams from a mood sensor and one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or an accelerometer monitoring a person, analyzing data sets collected from the person when the person is stressed and unstressed, analyzing the data sets, and determining a current stress index of the person based on the analysis.
[00011] US20190328301A1 ( By: HUAWEI) A psychological stress estimation method includes obtaining a physiological signal of a user corresponding to a current moment, determining a first stress indicator of the user based on the current moment and a cyclic stress model of the user, determining a second stress indicator of the user based on the physiological signal of the user corresponding to the current moment and an instantaneous stress model of the user, where the physiological signal is an input of the instantaneous stress model, and determining the current stress indicator of the user based on the first stress indicator and the second stress indicator.
[00012] US10687757B2 ( By: VITAL CONNECT) provides a method and system for determining psychological acute stress are disclosed. In a first aspect, the method comprises detecting a physiological signal using a wireless sensor device, determining a stress feature using a normalized heart rate and a plurality of heart rate variability (HRV) features, wherein the normalized heart rate and the plurality of heart rate variability features are calculated using the detected physiological signal, and determining a stress level using the stress feature to determine the psychological acute stress. In a second aspect, the system comprises a wireless sensor device that includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the wireless sensor device to carry out the steps of the method.
[00013] However, there are a number of drawbacks with current stress monitoring technology. Consequently, further technological improvement is needed in this area.

Summary
[00014] The present invention relates generally to monitoring of stress level of user.
[00015] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00016] The following paragraphs provide additional support for the claims of the subject application.
[00017] Embodiments of the present disclosure may include a system for assessing a person's stress level in real time including a wearable device may include one or more sensors interconnected to a person's body area, or arm area, or face and body area. Embodiments may also include one or more sensors interconnected to a person's body area, or skin area, or head and body area, or both.
[00018] Embodiments may also include a wireless transceiver interconnected to the one or more sensors. Embodiments may also include a processor included in the system capable of receiving a stream of medical data. Embodiments may also include a control unit may include an artificial intelligence and machine learning infrastructure that trains a machine-learned model to extract a number of physiological characteristics from the stream of medical data.
[00019] In some embodiments, the machine learning infrastructure trains the machine-learned model based on training data sets that define features stress level. Embodiments may also include analysing the number of physiological characteristics using a statistical analysis. Embodiments may also include executes a simulation process to calculate a person's current degree of stress based on a result of the statistical analysis.
[00020] In some embodiments, the physiological data may be captured with the aid of an array of sensors. In some embodiments, the plurality of physiological indicators may be selected from the group consisting of heart rate, respiration rate, blood pressure, blood oxygen saturation, blood oxygen saturation, and blood oxygen saturation. In some embodiments, the one or more sensors may include a wrist sensor and the pulse palpation rate may include a band of the wristwatch, the wristband, or fabric, which a wristband, or fabric may be fastened to. In some embodiments, the system as recited may include a smart phone connected to the processor adapted to receive the physiological data from the sensor and transmit the physiological data to the smart phone.
[00021] Embodiments of the present disclosure may also include a method for assessing a person's stress level in real time including receiving a stream of medical data from one or more sensors of a wearable device. Embodiments may also include extracting a number of physiological characteristics from the stream of medical data using an artificial intelligence and machine learning infrastructure. Embodiments may also include analysing the number of physiological characteristics using a statistical analysis. Embodiments may also include executes a simulation process to calculate a person's current degree of stress based on a result of the statistical analysis.
[00022] In some embodiments, the physiological data may be captured with the aid of an array of sensors. In some embodiments, the one or more sensors may include a wrist sensor and the pulse palpation rate may include a band of the wristwatch, the wristband, or fabric, which a wristband, or fabric may be fastened to. In some embodiments, the plurality of physiological indicators may be selected from the group consisting of heart rate, respiration rate, blood pressure, blood oxygen saturation, blood oxygen saturation, and blood oxygen saturation.
 
Brief Description of the Drawings
[00023] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00024] FIG. 1 is a block diagram illustrating a system, according to some embodiments of the present disclosure.
[00025] FIG. 2 is a block diagram further illustrating the system for stress monitoring, according to some embodiments of the present disclosure.
[00026] FIG. 3 is a block diagram further illustrating the system for psychological status detection FIG. 1, according to some embodiments of the present disclosure.
[00027] FIG. 4 is a flowchart illustrating a method for assessing a person's stress level in real time, according to some embodiments of the present disclosure.

Detailed Description
[00028] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00029] The use of the terms a and an and the and at least one and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term at least one followed by a list of one or more items (for example, at least one of A and B) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms comprising, having, including, and containing are to be construed as open-ended terms (i.e., meaning including, but not limited to,) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., such as) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00030] The present invention relates generally to monitoring of stress level of user. It is known in filed psychology that there is direct relation of stress to mental health and psychology. Furthermore, stress can cause changes in a person's behaviour and thought patterns, which can further exacerbate psychological issues. For example, stress can lead to negative thinking patterns and a lack of motivation, which can lead to feelings of hopelessness and further stress.
[00031] FIG. 1 is a block diagram that describes a system 100, according to some embodiments of the present disclosure. In some embodiments, the system 100 may include a wearable device 110, a wireless transceiver 130 interconnected to the one or more sensors 120, and a processor 140 included. The system 100 may also include one or more sensors 120 interconnected to a person's body area, or skin area, or head and body area, or both. The wearable device 110 may also include one or more sensors 112 interconnected to a person's body area, or arm area, or face and body area.
[00032] In some embodiments, the processor 140 may include a control unit 142 is arranged to receive a stream of medical data. The control unit 142 may include an artificial intelligence and machine learning infrastructure 144 that trains a machine-learned model to extract a number of physiological characteristics from the stream of medical data. The machine learning infrastructure trains the machine-learned model based on training data sets that define features stress level. Analysing the number of physiological characteristics using a statistical analysis. Executes a simulation process to calculate a person's current degree of stress based on a result of the statistical analysis. In some embodiments, the system 100 as recited.
[00033] FIG. 2 is a block diagram that further describes the system 100 from FIG. 1, according to some embodiments of the present disclosure. In some embodiments, the physiological data may be captured with the aid of an array of sensors.
[00034] FIG. 3 is a block diagram that further describes the system 100 from FIG. 1, according to some embodiments of the present disclosure. In some embodiments, the one or more sensors 120 may include a wrist sensor 314. The pulse palpation rate 320 may also include a band 322 of the wristwatch, the wristband, or fabric, which a wristband, or fabric may be fastened to.
[00035] FIG. 4 is a flowchart that describes a method for assessing a person's stress level in real time, according to some embodiments of the present disclosure. In some embodiments, at 410, the method may include receiving a stream of medical data from one or more sensors of a wearable device. At 420, the method may include extracting a number of physiological characteristics from the stream of medical data using an artificial intelligence and machine learning infrastructure. At 430, the method may include analysing the number of physiological characteristics using a statistical analysis. At 440, the method may include executes a simulation process to calculate a person's current degree of stress based on a result of the statistical analysis.
[00036] In some embodiments, the physiological data may be captured with the aid of an array of sensors. In some embodiments, the one or more sensors may comprise a wrist sensor and the pulse palpation rate comprises a band of the wristwatch, the wristband, or fabric, which a wristband, or fabric may be fastened to. In some embodiments, the plurality of physiological indicators may be selected from the group consisting of heart rate, respiration rate, blood pressure, blood oxygen saturation, blood oxygen saturation, and blood oxygen saturation.
[00037] In certain implementations, a wearable device may comprise one or more sensors coupled to a person's body region, arm area, or face and body area, allowing for real-time assessment of stress levels. An individual's body, skin, head, or both may be wired together to form a sensor network in certain embodiments.
[00038] In certain implementations, the one or more sensors are linked together through a wireless transceiver. In certain implementations, a medical data stream may be received by a processor built into the system. Physiological features may be extracted from a stream of medical data using a machine-learned model trained with the help of artificial intelligence and machine learning.
[00039] Some implementations of the machine learning infrastructure include training the machine-learned model using sets of data that specify the stress level of the features. Statistical analysis of a large set of physiological features is another possible aspect of embodiments. In certain implementations, the results of the statistical analysis are used to inform a simulation process that determines the individual's current stress level.
[00040] Physiological data may be collected in certain implementations with the help of a collection of sensors. Heart rate, respiratory rate, blood pressure, oxygen saturation, and so on may all be included in the set of several physiological indicators in various implementations. The band of a timepiece, a wristband, or fabric that may be affixed to a bracelet can be used to measure a user's pulse palpation rate in various implementations. The system as disclosed may also comprise a smart phone that is connected to the CPU and is configured to receive and send the physiological data from the sensor.
[00041] Receiving a stream of medical data from one or more sensors of a wearable device is one embodiment of the present disclosure that may be used to measure a person's stress level in real time. A variety of physiological parameters might be extracted from the flow of medical data utilising an AI and ML system in certain embodiments. Statistical analysis of a large set of physiological features is another possible aspect of embodiments. In certain implementations, the results of the statistical analysis are used to inform a simulation process that determines the individual's current stress level.
[00042] Physiological data may be collected in certain implementations with the help of a collection of sensors. The band of a timepiece, a wristband, or fabric that may be affixed to a bracelet can be used to measure a user's pulse palpation rate in various implementations. Heart rate, respiration rate, blood pressure, oxygen saturation, and oxygen saturation may all be included in the set of physiological indicators in various implementations.
[00043] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00044] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00045] The term non-transitory storage device or storage or memory, as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00046] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00047] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

 

Claims
I/We Claim:
1. A system for assessing a person's stress level in real time comprising: a wearable device comprises one or more sensors interconnected to a person's body area, or arm area, or face and body area; one or more sensors interconnected to a person's body area, or skin area, or head and body area, or both; a wireless transceiver interconnected to the one or more sensors; a processor included in the system capable of: receiving a stream of medical data; a control unit comprises an artificial intelligence and machine learning infrastructure that trains a machine-learned model to extract a number of physiological characteristics from the stream of medical data, wherein the machine learning infrastructure trains the machine-learned model based on training data sets that define features stress level; analysing the number of physiological characteristics using a statistical analysis; and executes a simulation process to calculate a person's current degree of stress based on a result of the statistical analysis.
2. The system of claim 1, wherein the physiological data is captured with the aid of an array of sensors.
3. The system of claim 1, wherein the one or more sensors comprises a wrist sensor and the pulse palpation rate comprises a band of the wristwatch, the wristband, or fabric, which a wristband, or fabric is fastened to.
4. The system of claim 2, wherein the plurality of physiological indicators are selected from the group consisting of heart rate, respiration rate, blood pressure, blood oxygen saturation, blood oxygen saturation, and blood oxygen saturation.
5. The system as recited in claim 1, further comprising a smart phone connected to the processor adapted to receive the physiological data from the sensor and transmit the physiological data to the smart phone.
6. A method for assessing a person's stress level in real time comprising: receiving a stream of medical data from one or more sensors of a wearable device; extracting a number of physiological characteristics from the stream of medical data using an artificial intelligence and machine learning infrastructure; analysing the number of physiological characteristics using a statistical analysis; and executes a simulation process to calculate a person's current degree of stress based on a result of the statistical analysis.
7. The method of claim 6, wherein the physiological data is captured with the aid of an array of sensors.
8. The method of claim 6, wherein the one or more sensors comprises a wrist sensor and the pulse palpation rate comprises a band of the wristwatch, the wristband, or fabric, which a wristband, or fabric is fastened to.
9. The method of claim 6, wherein the plurality of physiological indicators are selected from the group consisting of heart rate, respiration rate, blood pressure, blood oxygen saturation, blood oxygen saturation, and blood oxygen saturation.
 

WEARABLE DEVICE TO DETERMINE STRESS LEVEL
Abstract
A wearable device may comprise one or more sensors coupled to a person's body region, or arm area, or face and body area, to provide a real-time assessment of that person's stress level. In certain implementations, the one or more sensors are linked together through a wireless transceiver. In certain implementations, a medical data stream may be received by a processor built into the system. Physiological features may be extracted from a stream of medical data using a machine-learned model trained with the help of artificial intelligence and machine learning. , Claims:Claims
I/We Claim:
1. A system for assessing a person's stress level in real time comprising: a wearable device comprises one or more sensors interconnected to a person's body area, or arm area, or face and body area; one or more sensors interconnected to a person's body area, or skin area, or head and body area, or both; a wireless transceiver interconnected to the one or more sensors; a processor included in the system capable of: receiving a stream of medical data; a control unit comprises an artificial intelligence and machine learning infrastructure that trains a machine-learned model to extract a number of physiological characteristics from the stream of medical data, wherein the machine learning infrastructure trains the machine-learned model based on training data sets that define features stress level; analysing the number of physiological characteristics using a statistical analysis; and executes a simulation process to calculate a person's current degree of stress based on a result of the statistical analysis.
2. The system of claim 1, wherein the physiological data is captured with the aid of an array of sensors.
3. The system of claim 1, wherein the one or more sensors comprises a wrist sensor and the pulse palpation rate comprises a band of the wristwatch, the wristband, or fabric, which a wristband, or fabric is fastened to.
4. The system of claim 2, wherein the plurality of physiological indicators are selected from the group consisting of heart rate, respiration rate, blood pressure, blood oxygen saturation, blood oxygen saturation, and blood oxygen saturation.
5. The system as recited in claim 1, further comprising a smart phone connected to the processor adapted to receive the physiological data from the sensor and transmit the physiological data to the smart phone.
6. A method for assessing a person's stress level in real time comprising: receiving a stream of medical data from one or more sensors of a wearable device; extracting a number of physiological characteristics from the stream of medical data using an artificial intelligence and machine learning infrastructure; analysing the number of physiological characteristics using a statistical analysis; and executes a simulation process to calculate a person's current degree of stress based on a result of the statistical analysis.
7. The method of claim 6, wherein the physiological data is captured with the aid of an array of sensors.
8. The method of claim 6, wherein the one or more sensors comprises a wrist sensor and the pulse palpation rate comprises a band of the wristwatch, the wristband, or fabric, which a wristband, or fabric is fastened to.
9. The method of claim 6, wherein the plurality of physiological indicators are selected from the group consisting of heart rate, respiration rate, blood pressure, blood oxygen saturation, blood oxygen saturation, and blood oxygen saturation.